Statistics and agricultural

Role of statistical techniques in Agricultural Studies

1. Introduction In the modern world of computers and information technology, the importance of statistics is very well recogonised by all the disciplines. Statistics has originated as a science of statehood and found applications slowly and steadily in Agriculture, Economics, Commerce, Biology, Medicine, Industry, planning, education and so on. As on date there is no other human walk of life, where statistics cannot be applied.

2. Origin and growth of statistics The word ‘ Statistics’ and ‘ Statistical’ are all derived from the Latin word Status, means a political state. The theory of statistics as a distinct branch of scientific method is of comparatively recent growth. Research particularly into the mathematical theory of statistics is rapidly proceeding and fresh discoveries are being made all over the world.

3. Meaning of statistics Statistics is concerned with scientific methods for collecting, organising , summarising , presenting and analysing data as well as deriving valid conclusions and making reasonable decisions on the basis of this analysis. Statistics is concerned with the systematic collection of numerical data and its interpretation.The word ‘ statistic’ is used to refer to Numerical facts, such as the number of people living in particular area. The study of ways of collecting, analysing and interpreting the facts.

4. Definition Statistics are numerical statement of facts in any department of enquiry placed in relation to each other. - A.L. Bowley Statistics may be called the science of counting in one of the departments due to Bowley , obviously this is an incomplete definition as it takes into account only the aspect of collection and ignores other aspects such as analysis, presentation and interpretation.

5. Statistics and agricultural In agricultural research, for example, there are different statistical techniques for crop and animal research, for laboratory and field experiments, for genevic and physiological research, and so on. Although this diversit " indicates the ability of appropriate statistical techniques for most research problems, it also indicates the difficulty of matching the best technique to a specific experiment. Obviously, this difficulty increases as more procedures develop. Choosing the correct statistical procedure for a given experiment must be based on expertise in statistics and in the subject matter of the experiment. Thorough knowledge of only one of the two is not enough.

6. For most agricultural research institutions in the developing countries, the presence of trained statisticians is a luxury. Of the already small number of such statisticians, only a small fraction have the interest and experience agricultural research necessary for effective consultation. Thus, we feel the best alternative is to give agricultural researchers a statistical background so that they can correctly choose the statistical technique most appropriate for their experiment. For research institutions in the developed countries, the shortage of trained statisticians may not be as acute as in the developing countries. Nevertheless, the subject matter specialist must be able to communicate with the consulting statistician. Thus, for the developed-country researcher, this volume should help forge a closer researcher-statistician relationship.

7. Example In the early 1950s, a Filipino journalist, disappointed with the chronic shortage of rice in his country, decided to test the yield potential of existing rice cultivars and the opportunity for substantially increasing low yields in farmers' fields. He planted a single rice seed-from an ordinary farm-on a well-prepared plot and carefully nurtured the developing seedling to maturity. At harvest, he counted more than 1000 seeds produced by the single plant. The journalist concluded that Filipino farmers who normally use 50 kg of grains to plant a hectare, could harvest 50 tons (0.05 x 1000) from a hectare of land instead of the disappointingly low national average of 1.2 t/ha.

8. In agricultural research, the key questions to be answered are generally expressed as a statement of hypothesis that has to be verified or disproved through experimentation.These hypotheses are usually suggested by past experiences, observations, and, at times, by theoretical considerations. For example, in the case of the Filipino journalist, visits to selected farms may have impressed him as he saw the high yield of some selected rice plants and visualized the potential for duplicating that high yield uniformly on a farm and even over many farms. He therefore hypothesized that rice yields in farmers' fields were way below their potential and that, with better husbandry, rice yields could be substantially increased.

9. What do we mean by agricultural statistics The terms data, statistics and information are often used interchangeably but there are important distinctions. Data, statistics and information • What are they? • Why are they important? • Where do they come from? • What is the scope of agriculture stats and information? Data are the basic part of a broader information system. When statisticians produce data, they are trying to measure or count phenomena (things or activities) that are part of the real world. Data may be viewed as a lowest level of abstraction from which information and knowledge are derived. Examples of data: Number of cows on a farm ,Number of people in a household Number of children in a family In these cases, the data are derived by counting. If the question were: “How many dollars did you spend last year on improved seed?” the answer must be provided by a respondent who would look at records, or simply cite the number from memory. This is another example of measurement.

10. Statistics and data Statistics is also a mathematical science that focuses on the collection, analysis, interpretation or explanation, and presentation of data. 1 We often think of statistics as being produced by National Statistical Organizations (NSOs) but in fact they can be generated by any number of people. They can come from • Opinion polls • Surveys • Censuses • Administrative data (e.g., imports and exports)

11. General Data Dissemination System,

12. Agricultural data and information are required to support the following types of processes: • underpinning the planning processes; • compiling national accounts; • informing public policy analysis, debate and advice; • observing sector performance; • monitoring and evaluating the impact of policies and programmes and • enlightening the decision-making processes.

13. Examples of agriculture development objectives • Improving food supply (cereals, cashew nut, sugar, cotton) • Improving seeds • Providing access to fertilizer • Monitoring and controlling pests of basic crops and reducing animal mortality

14. Purpose of statistics statistics are produced and valued because they help decision makers and program managers make decisions and evaluate progress. It is these needs that must be kept in mind when planning and designing agriculture surveys.

15. STATISTISICAL COORDINATION • Legislation • Statistical priorities • Surveys and census must work together • Surveys, early warning systems and market information • Coordination improves the efficiency and usefulness of statistics o Classifications and definitions o Software tools o Statistical websites/portals • Sampling frames (The census of population is a key national resource) • Response burden • Specialized staff (survey design and sampling expertise) • Coordination with provincial bodies

16. The Stages of the Survey Process The statistical survey can be considered to fall into three parts all of which will be discussed in this paper • Planning and Design Phase • Implementation and Analysis • Dissemination and Archiving and Evaluation

17. Quality Control Survey Implementation Quality Evaluation Data collection Data capture and coding Correction and Cleaning Editing and Imputation Estimation, documentation Data Analysis